Dynamic Service Function Chaining by Resource Usage Learning in SDN/NFV Environment

Recently, to reflect diverse service requirements increasing with the popularization of various Internet devices, network functions virtualization (NFV) is attracting attention as the core technology of the next-generation network. In keeping with the progress of NFV, service function chaining (SFC) for specific network service appeared, and SFC refers to a technique for the sequential abstraction of service functions. In connection with the existing study's method for dynamic service chaining that dynamically generates a service chain through reinforcement learning, considering a node, at which service functions operate for efficient service chaining in the NFV environment, and the usage of resources such as CPU, memory, and network usage, this paper investigated a method for more stable dynamic service function chaining by flexibly calculating a fixed weight for the $r$ value, which was derived from an experiment, according to the amount of remaining resources at the relevant node.